Cloud-storage gateways represent an interesting intermediate point between current storage architectures and future cloud-only systems. A gateway appears to clients as if it is a typical network file or block server, speaking protocols such as NFS , CIFS, or iSCSI, and thus enabling ready deployment within existing infrastructures; however, on the backend, the gateway is connected to a cloud storage service such as Amazon’s S3 or Google Storage, thus adding new functionality (automated off-site backup, cross-site sharing) and giving rise to new opportunities for data access and management.
In this proposal, we describe our proposed research to increase the intelligence of gateways so as to improve performance, increase reliability, and lower costs. Our initial focus will be on block-storage servers; however, much of our research will be applicable to both block-level and file-level gateways. To improve block-level gateways, we plan to develop and evaluate a series of block-inference techniques; by peering into the block stream and interpreting the contents of blocks, we will achieve tremendous improvements in gateway performance, reliability, and cost.